Digital design of pharmaceutical processes using PharmaPy

No. of participants: 30
Duration: 4 hours
Requirements: Basic familiarity with Python
Workshop coordinator: Zoltan K Nagy, Purdue University
Participants: Rex Reklaitis (Purdue University), Yash Barhate (Purdue University), Jungsoo Rhim (Purdue, University), Mohammad Shahab (Purdue University)

The use of digital tools in pharmaceutical manufacturing has gained traction over the past two decades. Whether supporting regulatory filings or attempting to modernize manufacturing processes to adapt to the ever evolving Industry 4.0 standards, engineers entering the workforce must exhibit a proficiency in modeling, simulation, optimization, data processing, and other digital analysis techniques.

We propose to have a 4-hour workshop, that will cover general concepts of pharmaceutical process modeling and digital design using the open-source tool, PharmaPy.

The workshop will describe the main features of PharmaPy, and how it can be used for process simulation, parameter estimation, and simulation-based optimization considering single unit operations as well as integrated process flowsheets. Case studies with applications in process synthesis and techno-economic analyses of process flowsheets including comparison of end-to-end batch, continuous and hybrid manufacturing routes will be included. All case studies will be taught using a web-based interactive computing platform.

The main objective of the workshop is to increase understanding of pharmaceutical processes by in-silico process analysis using PharmaPy. After attending the workshop, the participants are expected to:
1. Have a clear understanding of the modeled phenomena in the unit operations (UOs) supported by PharmaPy
2. Use PharmaPy's modeling objects to analyze individual UOs
3. Perform parameter estimation by integrating UO objects into a simulation executive
4. Connect multiple UOs in different operating modes to analyze pharmaceutical manufacturing
5. Perform process optimization by clear understanding of problem formulation and use of user-defined simulation callbacks
6. Propose approaches to improve process performance using PharmaPy

Topics to be included:
1. Introduction to PharmaPy
a. Contextualization/motivation: PharmaPy
b. PharmaPy architecture: fundamentals
2. Introduction to modeling of pharmaceutical processes
a. Reactors
b. Crystallization
c. Filtration, drying
d. Feeder, blender, tableting
3. Examples of applications of PharmaPy
a. Digital twin development for reactors and design space analysis
b. Digital design and nonlinear real-time optimization of an integrated crystallization-filtration-drying system
c. Techno-economic analysis of batch, continuous and hybrid manufacturing systems
d. Process synthesis of optimal pharmaceutical manufacturing systems

Hands-on workshop
4. Case study I. Parameter estimation for reaction systems
a. Batch reactor modeling in PharmaPy: unit operation and kinetic models
b. SimulationExec class: use of the simulation executive to run parameter estimation
c. Parameter estimation
5. Case study II: Process optimization in PharmaPy
a. Modelling of crystallization systems: fundamentals
b. Formulation of an optimization problem for crystal size maximization
c. Creation of PharmaPy callback functions to enable optimization
d. Solving the optimization problem and analyzing the converged results
6. Case study III: Flowsheet analysis with PharmaPy
a. PharmaPy flowsheet capabilities
b. Creating a flowsheet graph
c. Running flowsheet in simulation mode
d. Function callbacks and optimization

Resources:
Training material and PharmaPy installation is available at: http://www.pharmapy.co
Participants are requested to download and install Python and PharmaPy before the workshop following the instructions on the website.

Papers with examples:
1. D. Casas-Orozco, D. Laky, V. Wang, M. Abdi, X. Feng, E. Wood, G.V. Reklaitis, Z.K. Nagy, Techno-economic analysis of dynamic, end-to-end optimal pharmaceutical campaign manufacturing using PharmaPy, AICHE J., 69 (9), e18142, 2023.
2. D. Casas-Orozco, D. Laky, J. Mackey, G.V. Reklaitis, Z.K. Nagy, Reaction kinetics determination and uncertainty analysis for the synthesis of the cancer drug lomustine, Chem. Eng. Sci., 275, 118591, 2023.
3. I. Hur, D.M. Casas-Orozco, D.J. Laky, F. Destro, Z.K. Nagy, Digital design of an integrated purification system for continuous pharmaceutical manufacturing, Chem. Eng. Sci, 285, 119534, 2024.
4. D. Laky, D. Casas-Orozco, C.D. Laird, G.V. Reklaitis, Z.K. Nagy, Simulation-optimization framework for the digital design of pharmaceutical processes using Pyomo and PharmaPy, Ind. Eng. Chem. Res., 61 (43), 16128-16140, 2022.
5. D.M. Casas-Orozco, D.J. Laky, V. Wang, M. Abdi, X. Feng, E. Wood, C. Laird, G.V. Reklaitis, Z.K. Nagy, PharmaPy: an object-oriented tool for the development of hybrid pharmaceutical flowsheets, Comp. & Chem. Eng., 107408, 2021.

Zoltan K Nagy is the Arvind Varma Professor of Chemical Engineering in the Davidson School of Chemical Engineering at Purdue University, and Professor of Process Systems Engineering at Loughborough University, UK.
He received his B.S. (1994), M.Sc (1995) and PhD (2001) degrees from the "Babes-Bolyai" University of Cluj, Romania. His research focuses on pharmaceutical systems engineering, process intensification and advanced process control, crystallization modeling and control approaches and advanced control of particulate systems, with application to pharmaceuticals, fine chemicals, food and energetic materials. He has published over 250 journal papers, 300 conference proceeding papers, 6 patents and cofounded three companies.
He has graduated over 50 PhD students and postdocs in the UK and USA. He has received awards in the areas of crystallization and control from IEEE, IFAC, European Federation of Chemical Engineering, Royal Academy of Engineering and the European Research Council and he was the recipient of the AIChE's Excellence in Process Development Research Award (2018) and the Pharmaceutical Discovery Development and Manufacturing (PD2M) Forum Award for Outstanding Contribution to QbD for Drug Substance (2019).

Gintaras.V. (Rex) Reklaitis is Gedge Distinguished Professor of Chemical Engineering at Purdue University with courtesy appointment in Industrial and Molecular Pharmaceutics. His research expertise lies broadly in process systems engineering, with recent focus on applications to improve pharmaceutical product design, development, manufacture and delivery.
Specific themes include continuous drug substance and drug product manufacturing, digital twin development and exploitation, additive manufacturing of oral dosage products and Bayesian approaches to precision dosing. Educated at the Illinois Institute of Technology (BS ChE), and Stanford University (MS & PhD), he is a member of the US National Academy of Engineering, fellow of AIChE and AAAS, and past Editor-in-Chief of Computers & Chemical Engineering.
He is recipient of the John M Prausnitz, Warren K Lewis, Van Antwerpen and Computing in Chemical Engineering Awards of AIChE , the Pruitt Award (CCR), and the Long Term Achievements in Computer Aided Process Engineering Award (EFChE).
He has served on the Board of Directors of AICHE, the Council for Chemical Research and the CACHE Corporation and continues to serve on the editorial boards of several journals. He recently chaired the National Academies Committee to Identify Innovative Technologies to Advance Pharmaceutical Manufacturing. He has published over 350 papers and book chapters and edited/authored nine books.

Yash Barhate is a 3rd-year PhD candidate at Purdue University working in Prof. Zoltan Nagy's research group. His work revolves around the mathematical modeling and process optimization of integrated process systems, with a particular emphasis on crystallization processes in pharmaceutical manufacturing.

Jungsoo Rhim is a 4th year graduate student in the doctoral program being co-advised in the Aeronautics & Astronautics Engineering and Chemical Engineering departments. His main field of research is the development of technoeconomic cost models for pharmaceutical processes and system engineering. In his free time he enjoys baking and reading as a hobby.

Mohammad Shahab is a postdoctoral research associate at Purdue University. His research area includes digital design of integrated downstream pharmaceutical manufacturing systems, specifically on process modeling, optimization and condition monitoring of direct compaction and dry granulation processes.